{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt(\"Photolysis.dat\", dtype=str)\n",
    "Header = (data[0,:]).tolist()\n",
    "solTchem = (data[1:,:]).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "campsol = np.loadtxt(\"photolysis_results.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_indx = Header.index('A')\n",
    "B_indx = Header.index('B')\n",
    "C_indx = Header.index('C')\n",
    "t_indx = Header.index('t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Cells: 1\n",
      "Number of time iterations: 129.0\n"
     ]
    }
   ],
   "source": [
    "niterT, Nvars = np.shape(solTchem)\n",
    "Nsamples = len(np.where(solTchem[:,0]==-1)[0])\n",
    "print('Number of Cells:',Nsamples)\n",
    "print('Number of time iterations:',niterT/Nsamples)\n",
    "solTchem = solTchem.reshape( int(niterT/Nsamples), Nsamples,Nvars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9850222fa0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp=0 # cell number 0, camp only saved cell No 1 \n",
    "plt.figure()\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx], label=\"A\")\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx], label=\"B\")\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,C_indx], label=\"C\")\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f98203d1370>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,1],label='CAMP')\n",
    "plt.plot(campsol_old[:,0],campsol_old[:,1],label='CAMP old')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of A [mol/m3]')\n",
    "plt.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f98600c7dc0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,3],label='CAMP')\n",
    "plt.plot(campsol_old[:,0],campsol_old[:,3],'ro',label='CAMP old')\n",
    "# plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of B [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f985032b2b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ezE0Tvkltr36aVkJEilrUNgJpYNWmf3Bf2SmU/uHLfOFUTcUkIsVLJYIDNO+d9wE4SYvUi0iRi1QiMLM+wJeBssxr3P0L8YRV+F56p5ZuJR04ql/3fIciItIqUauGHgH+CvwJ+Ci+cIrHS++8z4llPWnfzvIdiohIq0RNBF3d/YZYIykiG7bs4O0NH3JJxeH5DkVEpNWithH80czGxxpJEXk5bB84sVztAyJS/KImgusIksF2M9sSvjbHGVjBqqzklE8fz9u3fI6Rp43URHMiUvSiDijrFncgRSGcaK5H3RxD1dWaaE5Eil7k7qNmdq6Z3Rq+PhtnUAVLE82JSAJFSgRmdjNB9dDS8HVduC9dNNGciCRQ1F5D44GR7r4HwMzuB14BbowrsIJUWgorVza+X0SkSLVkZHGPjPeH5DiO4jBjBjs6ldTfp4nmRKTIRU0EPwZeMbP7wtLAAuCm+MIqTH7ppUz/7HVs7N1fE82JSGJE7TU028yeAU4ADLjB3dfGGVghWlG7jdlDT2PEDV9j4omqDhKRZGi2RGBmR4U/RwH9gRrgXWBAuC9VFr67EYCRpT3yG4iISA5lKxF8C5gC/LSRYw6ckfOICtgr1Zs4qFN7hvbVsAoRSY5mE4G7h6OlONvdt2ceM7OSRi6hwTlnAbcB7YHfuHujXU7N7ATgReASd38wSuD5sPDdTRw3qIcmmhORRInaWPx8xH17mVl74A7gbGAYMMnMhjVx3i3AExFjyYvtuz5i6erNqhYSkcRptkRgZv2AgUAXM/sEQUMxQHega5bPPhFY7u5vh59VBZxHMCAt09eB3xM0RBesJas/YPce5xOH98h3KCIiOZWtjeBM4EpgEPCzjP1bgO9muXYgQcNynRrgpMwTzGwgMIGgraHJRGBmUwjaKijN0+CtV6o3AWooFpHkabZqyN3vd/dPAVe6+6cyXue6+0NZPruxinRvsP0Lgq6ozS524+6z3L3C3Sv69OmT5WtjUFnJhAtO4e1bPkffY4/SjKMikihRxxH83szOAYYDJRn7f9DMZTVA5sotg4DVDc6pAKrMDKA3MN7Mdrv7H6LE1SbCGUd71U02t3KlZhwVkUSJOuncncAlBPX5BlwEDM5y2TxgqJmVm1knYCLwaOYJ7l7u7mXuXgY8CHytoJIAaMZREUm8qL2Gxrj7FcBGd/8+MJr6v+3vx913A1MJegO9Bvy3uy8xs6vN7OrWBN2mNOOoiCRc1NlH68YQbDOzAUAtUJ7tInd/HHi8wb47mzj3yoixtC3NOCoiCRe1RPCYmfUAZgJ/A1YAs2OKqbBoxlERSbisicDM2gFPufsmd/89QdvAUe7+vdijKwSTJ/PjCd+ktnc/zTgqIomUtWrI3feY2U8J2gVw9x3AjrgDKxSbtu3kvrJTOGzOF/nq2CH5DkdEJOeiVg39r5n9k4X9PNNkyerNABwzsHueIxERiUfUxuJvAQcBu81sO0EXUnf3xD8dl6z+AIDhA9K5KJuIJF/UAWWpnXd58arNDDikhJ4Hdcp3KCIisYg6oOypKPuSaPHqDxg+UKUBEUmubLOPlhDMMtrbzA6l/uyjA2KOLe8+3LGbd977kPNGDMx3KCIisclWNfQV4BsED/0F7EsEmwnWGki019Zsxl0NxSKSbNlWKLsNuM3Mvu7uv2yjmArG4lVqKBaR5IvURuDuvzSzMWZ2qZldUfeKO7i8qqzk3PODqacPO+5ITT0tIokVqdeQmT0ADAEWAnVrBzjwX/GElWfh1NM962Ydra7W1NMikljm3nCtmEZOMnsNGOZRTo5ZRUWFz58/P94vKStrfKK5wYNhxYp4v1tEJAZmtsDdKxo7FnVk8WKgX+5CKnCaelpEUiTqyOLewFIze5mMeYbc/dxYoso3TT0tIikSNRFMjzOIgjNjBju/+CU67di+b5+mnhaRhIraa+hZgjUIOobv5xGsS5BMkydz12U3sv7QwzT1tIgkXtReQ18GpgA9CXoPDQTuBMbFF1p+3Vs2hhX/eT4zLxqR71BERGIVtbH4GuAUghHFuPubQN+4gsq3DVt28N7WnRzZL7Vz7YlIikRNBDvcfWfdhpl1IBhHkEjL1m4B4Oj+mlpCRJIvaiJ41sy+C3Qxs88Ac4DH4gsrv15fGyxGoxKBiKRB1ERwI7ABeJVgIrrHgX+NK6h8e33tFnof3JneB3fOdygiIrGL2n20C3CPu98FYGbtw33b4gosn15fu5mjVBoQkZSIWiJ4iuDBX6cL8Kfch5N/uz/aw5vrtioRiEhqRE0EJe6+tW4jfN81npDya0XtNnbs3qP2ARFJjaiJ4EMzG1W3YWbHA/+IJ6T8Uo8hEUmbqG0E3wDmmNnqcLs/cEksEeXZ62s3087gY30PzncoIiJtIuoUE/OAo4CvAl8Djnb3BXEGlheVlXz+0rEsv/lzlAwdosVoRCQVopYIAE4AysJrPmFmuHtyFqYJF6PpXbcYzcqVWoxGRFIhUokgXKHsVuBUgoRwAtDoAgdFa9o02NagN+y2bcF+EZEEi1oiqOAAVigzs7OA24D2wG/c/eYGxycDN4SbW4GvuvvfW/IdOaPFaEQkpWJboSwcdHYHcDYwDJhkZsManPYO8El3Pw74ITCrJd+RU00tOqPFaEQk4aImgroVyp4ws0frXlmuORFY7u5vhxPWVQHnZZ7g7s+7+8Zw80VgUEuCz6kZM9jZuaT+Pi1GIyIpEOcKZQOBdzO2a4CTmjn/i8D/NHbAzKYQrIdAaVy/oU+ezF1PL+eih35N303rg5LAjBlqKBaRxIuUCNz9WTM7jKCRGOBld1+f5TJr7KMaPdHsUwSJ4NQmvn8WYbVRRUVFbNNf31d+Cm/feT4/vViL0YhIekTtNXQx8DJwEXAx8JKZXZjlshrg8IztQcDqhieZ2XHAb4Dz3L02Sjxx2PjhTjZs2cGR/TSQTETSJWrV0DTghLpSgJn1IZh07sFmrpkHDDWzcmAVMBG4NPMEMysFHgIud/c3Whh7Tr2xLpha4uOHaY4hEUmXqImgXYOqoFqylCbcfbeZTQWeIOg+eo+7LzGzq8PjdwLfA3oBvzIzgN3unpfxCUoEIpJWURPBXDN7Apgdbl9CEw27mdz9cYJFbDL33Znx/kvAlyLGEKs31m2lW+cO9D+kJPvJIiIJErWx+Hozu4CgMdeAWe7+cKyRtbFl67bw8X7dCEsmIiKp0Wz1jpl9zMxOAXD3h9z9W+7+TaDWzIa0SYRtwN15c90WPn6YGopFJH2y9Rr6BbClkf3bwmOJsGHrDjZu26X2ARFJpWyJoMzdFzXc6e7zCWYiTYQ31gaLrykRiEgaZUsEzbWcdmnmWFFRjyERSbNsiWCemX254U4z+yKQmIVp3li3hZ4HdaL3wZ3yHYqISJvL1mvoG8DD4XTRdQ/+CqATMCHGuNpOZSXf+vq3uWnjeuxuzS8kki+7du2ipqaG7du35zuUolZSUsKgQYPo2LFj5GuaTQTuvg4YE84FdEy4+/+5+58PPMwCUlmJT5lCX61KJpJ3NTU1dOvWjbKyMnXjPkDuTm1tLTU1NZSXl0e+LuqaxU+7+y/DVzKSAMC0aZhWJRMpCNu3b6dXr15KAq1gZvTq1avFpaqo6xEkk1YlEykoSgKtdyB/hulOBFqVTEQk5Ylgxgx2dNKqZCKyz9q1a5k4cSJDhgxh2LBhjB8/njfeCCZH/vnPf05JSQkffPDB3vOfeeYZzIy77757775XXnkFM+PWW28F4Morr6S8vJyRI0cyatQoXnjhhba9qSzSnQgmT2bmP32b2l79wAwGD4ZZs9RQLJJS7s6ECRMYO3Ysb731FkuXLuWmm25i3bp1AMyePZsTTjiBhx+uP9Xasccey+9+97u921VVVYwYUX+Bq5kzZ7Jw4UJuvvlmvvKVr8R/My0QdfbRRPpwx27uHjya7r/7PNeOG5rvcEQk9P3HlrB09eacfuawAd35988Nb/acp59+mo4dO3L11Vfv3Tdy5EgA3nrrLbZu3crMmTO56aabuPLKK/eeU1payubNm1m3bh19+/Zl7ty5jB8/vtHvOP3001m+fHmr7yeXUl0ieH3tFtzh6P7d8x2KiBSAxYsXc/zxxzd6bPbs2UyaNInTTjuNZcuWsX59/dV6L7zwQubMmcPzzz/PqFGj6Ny5c6Of89hjj3HsscfmPPbWSHWJYOma4DeOYQOUCEQKSbbf3POhqqqKhx9+mHbt2nHBBRcwZ84crrnmmr3HL774Yi655BJef/11Jk2axPPPP1/v+uuvv54f/ehH9OnTp157QiFIdyJYvZlDunRkgBajERFg+PDhPPjg/ivwLlq0iDfffJPPfOYzAOzcuZMjjjiiXiLo168fHTt25Mknn+S2227bLxHMnDmTCy/MttR7fqS6amjpms0M699dfZdFBIAzzjiDHTt2cNddd+3dN2/ePK677jqmT5/OihUrWLFiBatXr2bVqlWsXLmy3vU/+MEPuOWWW2jfvn1bh94qqU0EH+1xlq3drGohEdnLzHj44Yd58sknGTJkCMOHD2f69Ok888wzTJhQf3q1CRMmUFVVVW/fmDFjOP/889sw4twwd893DC1SUVHh8+fPb/XnLF+/lU//7FluvWgEFx4/KAeRiUhrvPbaaxx99NH5DiMRGvuzNLMF7l7R2PmpLRHsbShWjyERSbn0JoLVm+nY3vhYX61TLCLplt5EsGYzQ/t2o1OH1P4RiIgAaew+WlkJ06Zx38pqNvXuB71nakoJEUm1dCWCyspg4Zlt22gH9HxvjRaiEZHUS1e9yLRpwcIzmbQQjYikXLoSgRaiEUmOykooK4N27YKflZWt/sjmpqCG/ExDPX369L2fk2nFihUcc8wxjVzRculKBFqIRiQZ6qp5V64E933rjbciGWSbghqSOw11uhLBjBnsLulSf58WohEpPjFU8zY1BfVpp50G7JuG+kc/+hGzZ8+ud21paSnbt29n3bp1uDtz587l7LPPbvR7mpqGeuXKlYwbN47jjjuOcePGUd1ITcWCBQsYMWIEo0eP5o477jjge20oXYlg8mTumPQvrO1xGK6FaESKVwzVvM1NQQ3xT0M9depUrrjiChYtWsTkyZO59tpr9zvnqquu4vbbb8/5CmepSgTVtdv4ed8T+P1Dz2F79sCKFUoCIsUoD9W8VVVVTJw4sd401Jkuvvhi5syZszdhNHT99dczcuRIZs2a1eg01C+88AKXXnopAJdffjnPPfdcveMffPABmzZt4pOf/OTec3Il1kRgZmeZ2TIzW25mNzZy3Mzs9vD4IjMbFUsgYaPS4X0O5rlfX8Wly/8ay9eISBuZMSOo1s3Uymre4cOHs2DBgkaPZU5DXVZWRlVV1X7VQ5nTUI8bN26/z6hrI3jyyScjNfI2nBXZ3WObKTm2RGBm7YE7gLOBYcAkMxvW4LSzgaHhawrw65wHktGoZO4M2ryBQ78xNSc9DEQkTyZPDqp1Bw/O2XrjTU1B/eyzzzJ79uzYp6EeM2bM3tlMKysrOfXUU+sd79GjB4cccsjekkJlDp9hcZYITgSWu/vb7r4TqALOa3DOecB/eeBFoIeZ9c9pFBo7IJJMkycH1bs5quZtagrqAQMGUFVVFfs01Lfffjv33nsvxx13HA888AC33Xbbfufce++9XHPNNYwePZouXbo08ikHJrZpqM3sQuAsd/9SuH05cJK7T80454/Aze7+XLj9FHCDu89v8FlTCEoMlJaWHt8wCzerXbuge9n+AQb/gESkIGga6twppGmoG6vMavhEjnIO7j7L3SvcvaJPnz4ti0JjB0REmhVnIqgBDs/YHgSsPoBzWieGRiURkSSJMxHMA4aaWbmZdQImAo82OOdR4Iqw99DJwAfuvianUcTQqCQi8Si2FRML0YH8GcY2+6i77zazqcATQHvgHndfYmZXh8fvBB4HxgPLgW3AVbEEM3myHvwiBa6kpITa2lp69eoVWzfJpHN3amtrKSkpadF1qV2zWEQKy65du6ipqWH79u35DqWolZSUMGjQIDp27Fhvf3ONxelaj0BEClbHjh0pLy/PdxiplKopJkREZH9KBCIiKadEICKSckXXWGxmG4AWDC2upzfwXg7DKQa653TQPadDa+55sLs3OiK36BJBa5jZ/KZazZNK95wOuud0iOueVTUkIpJySgQiIimXtkQwK98B5IHuOR10z+kQyz2nqo1ARET2l7YSgYiINKBEICKScqlJBGZ2lpktM7PlZnZjvuOJg5kdbmZPm9lrZrbEzK4L9/c0syfN7M3w56H5jjWXzKy9mb0SrniXhvvtYWYPmtnr4d/16BTc8zfDf9OLzWy2mZUk7Z7N7B4zW29mizP2NXmPZvad8Hm2zMzObM13pyIRmFl74A7gbGAYMMnMhuU3qljsBr7t7kcDJwPXhPd5I/CUuw8Fngq3k+Q64LWM7aTf723AXHc/ChhBcO+JvWczGwhcC1S4+zEE09pPJHn3fB9wVoN9jd5j+P96IjA8vOZX4XPugKQiEQAnAsvd/W133wlUAeflOaacc/c17v638P0WggfEQIJ7vT887X7g/LwEGAMzGwScA/wmY3eS77c7cDpwN4C773T3TST4nkMdgC5m1gHoSrCSYaLu2d3/ArzfYHdT93geUOXuO9z9HYI1XU480O9OSyIYCLybsV0T7kssMysDPgG8BBxWt/Jb+LNvHkPLtV8A/wLsydiX5Ps9AtgA3BtWh/3GzA4iwffs7quAW4FqYA3BSob/S4LvOUNT95jTZ1paEkFjyx0ltt+smR0M/B74hrtvznc8cTGzzwLr3X1BvmNpQx2AUcCv3f0TwIcUf5VIs8J68fOAcmAAcJCZXZbfqPIup8+0tCSCGuDwjO1BBEXLxDGzjgRJoNLdHwp3rzOz/uHx/sD6fMWXY6cA55rZCoLqvjPM7Lck934h+Ldc4+4vhdsPEiSGJN/zp4F33H2Du+8CHgLGkOx7rtPUPeb0mZaWRDAPGGpm5WbWiaCR5dE8x5RzFiz0ejfwmrv/LOPQo8Dnw/efBx5p69ji4O7fcfdB7l5G8Hf6Z3e/jITeL4C7rwXeNbMjw13jgKUk+J4JqoRONrOu4b/xcQTtX0m+5zpN3eOjwEQz62xm5cBQ4OUD/hZ3T8ULGA+8AbwFTMt3PDHd46kExcNFwMLwNR7oRdDj4M3wZ898xxrDvY8F/hi+T/T9AiOB+eHf8x+AQ1Nwz98HXgcWAw8AnZN2z8BsgjaQXQS/8X+xuXsEpoXPs2XA2a35bk0xISKScmmpGhIRkSYoEYiIpJwSgYhIyikRiIiknBKBiEjKKRGIiKScEoGklpn1MrOF4Wutma0K3281s1/F8H33mdk7ZnZ1M+ecZmZLM6ciFombxhGIAGY2Hdjq7rfG+B33EQx6ezDLeWXhecfEFYtIJpUIRBows7EZi9xMN7P7zex/zWyFmV1gZj8xs1fNbG44txNmdryZPWtmC8zsibr5YbJ8z0XhQit/N7O/xH1fIk1RIhDJbgjBmgfnAb8Fnnb3Y4F/AOeEyeCXwIXufjxwDzAjwud+DzjT3UcA58YSuUgEHfIdgEgR+B9332VmrxKsjjU33P8qUAYcCRwDPBnMiUZ7gjljsvk/4D4z+2+CGTVF8kKJQCS7HQDuvsfMdvm+hrU9BP+HDFji7qNb8qHufrWZnURQ2lhoZiPdvTaXgYtEoaohkdZbBvQxs9EQrAlhZsOzXWRmQ9z9JXf/HvAe9eeXF2kzKhGItJK77zSzC4HbzewQgv9XvwCWZLl0ppkNJShRPAX8PdZARZqg7qMibUTdR6VQqWpIpO18APww24Ay4DGCqiKRNqESgYhIyqlEICKSckoEIiIpp0QgIpJySgQiIin3/wGLkOrcc0bsIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,5],label='CAMP')\n",
    "plt.plot(campsol_old[:,0],campsol_old[:,5],'ro',label='CAMP old')\n",
    "# plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,C_indx],\"*\",label='TChem')\n",
    "\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of B [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
